code: def test_forward(self, model, tokens, use_pf, fp16):
      output = model(
          **tokens,
          decoder_input_ids=tokens["input_ids"],
          output_hidden_states=True,
      )
      torch.save(
          output.encoder_hidden_states,
          f"output-encoder-{'pf' if use_pf else 'non-pf'}-{'fp16' if fp16 else 'fp32'}.pt",
      )
      torch.save(
          output.decoder_hidden_states,
          f"output-decoder-{'pf' if use_pf else 'non-pf'}-{'fp16' if fp16 else 'fp32'}.pt",
      )
      print()
  
file_path: parallelformers/tests/seq2seq_lm.py
imports: import os
  import unittest
  from argparse import ArgumentParser
  import torch
  from transformers import AutoModelForSeq2SeqLM, AutoTokenizer
  from parallelformers import parallelize
label_desc: Use image node_id 2 for calling the TestForCausalLM obj's underlying member method code with example usage: obj.test_forward(model, tokens, use_pf, fp16) without return types